Tracxn Research — Enterprise Collaboration Landscape, November 2016Tracxn
Investments in the year to date rose by 10% YoY, to $1.1B, as the enterprise collaboration space goes through a shift from a document centric to a conversational model.
Tracxn Research - Insurance Tech Landscape, February 2017Tracxn
Round count hit an all-time sector peak in seed (81), early (72), and late stage (17), with late stage deal activity registering the most growth (46%).
Tracxn Research — Enterprise Collaboration Landscape, November 2016Tracxn
Investments in the year to date rose by 10% YoY, to $1.1B, as the enterprise collaboration space goes through a shift from a document centric to a conversational model.
Tracxn Research - Insurance Tech Landscape, February 2017Tracxn
Round count hit an all-time sector peak in seed (81), early (72), and late stage (17), with late stage deal activity registering the most growth (46%).
Tracxn Research: Healthcare Analytics Startup Landscape, July 2016Tracxn
Government launches the first of what would become 3,000,000 acres of land confiscations from Māori in Waikato, Taranaki, Bay of Plenty, and Hawke's Bay.
Tracxn Research — Big Data Infrastructure Landscape, September 2016Tracxn
Following a rather muted 2015, the year 2016 witnessed an impressive bounce back by the big data sector, with a total funding of $1.1B secured in 37 rounds.
Tracxn Research — Business Intelligence Landscape, December 2016Tracxn
Despite a YoY decline in seed and early stage rounds and capital invested, 2016 was the fourth consecutive year in which the sector witnessed $1B+ plus investments.
Tracxn Wind Energy Landscape Report July 2016Tracxn
The top business models are built around power generation (Greenko Group, ReNew Power Ventures), manufacturing (Clipper Wind, Suzlon), and service providers (SITAC Renewable energy, PNE Wind).
Tracxn Research — Immuno-Oncology Landscape, September 2016Tracxn
In 2015/16, five startups in this space — Stemcentrx, Gritstone Oncology, Hengrui Therapeutics, and Zai Lab secured big ticket funding rounds of $100 million and above.
Tracxn Insurance Tech Landscape June 2016 ReportTracxn
Business models like Internet first insurers, enablers, distribution platforms, and P2P insurance, all have seen an uptick in number of companies founded.
Tracxn Startup Research — Life Sciences Landscape, October 2016Tracxn
There were 20+ acquisitions in this space in 2016; IBM’s $2.6B acquisition of Truven Analytics, and Affymetrix’s acquisition by Thermo Fisher Scientific for $1.6B were the largest M&A events for the sector this year.
Tracxn Startup Research: Data as a Service Landscape, August 2016Tracxn
The top three funded sub-sectors till date are market intelligence (149 investments, $1.3B), financial data providers (158 investments, $1.2B), and geospatial data providers.
Tracxn Research — Mobile Dev Tools Landscape, November 2016Tracxn
$747M has been invested in the mobile devtools space in 2015/16, with investments in MADP (mobile application development platforms, $128M), and app prototyping ($56M) startups peaking this year.
Tracxn Research Ad Tech Landscape, October 2016Tracxn
Investments in the adtech industry are driven by mobile, digital video, cross-screen advertising and programmatic buying targeting specific sectors or audience groups.
Tracxn Research — Enterprise Storage Landscape, November 2016Tracxn
Startups which offer backup storage solutions for enterprises emerged as the top funded business model of the year in the enterprise storage sector, accounting for 40% ($371M) of the total funding secured by the sector. The backup storage business model also witnessed a 70% YoY rise.
Tracxn Research: Healthcare Analytics Startup Landscape, July 2016Tracxn
Government launches the first of what would become 3,000,000 acres of land confiscations from Māori in Waikato, Taranaki, Bay of Plenty, and Hawke's Bay.
Tracxn Research — Big Data Infrastructure Landscape, September 2016Tracxn
Following a rather muted 2015, the year 2016 witnessed an impressive bounce back by the big data sector, with a total funding of $1.1B secured in 37 rounds.
Tracxn Research — Business Intelligence Landscape, December 2016Tracxn
Despite a YoY decline in seed and early stage rounds and capital invested, 2016 was the fourth consecutive year in which the sector witnessed $1B+ plus investments.
Tracxn Wind Energy Landscape Report July 2016Tracxn
The top business models are built around power generation (Greenko Group, ReNew Power Ventures), manufacturing (Clipper Wind, Suzlon), and service providers (SITAC Renewable energy, PNE Wind).
Tracxn Research — Immuno-Oncology Landscape, September 2016Tracxn
In 2015/16, five startups in this space — Stemcentrx, Gritstone Oncology, Hengrui Therapeutics, and Zai Lab secured big ticket funding rounds of $100 million and above.
Tracxn Insurance Tech Landscape June 2016 ReportTracxn
Business models like Internet first insurers, enablers, distribution platforms, and P2P insurance, all have seen an uptick in number of companies founded.
Tracxn Startup Research — Life Sciences Landscape, October 2016Tracxn
There were 20+ acquisitions in this space in 2016; IBM’s $2.6B acquisition of Truven Analytics, and Affymetrix’s acquisition by Thermo Fisher Scientific for $1.6B were the largest M&A events for the sector this year.
Tracxn Startup Research: Data as a Service Landscape, August 2016Tracxn
The top three funded sub-sectors till date are market intelligence (149 investments, $1.3B), financial data providers (158 investments, $1.2B), and geospatial data providers.
Tracxn Research — Mobile Dev Tools Landscape, November 2016Tracxn
$747M has been invested in the mobile devtools space in 2015/16, with investments in MADP (mobile application development platforms, $128M), and app prototyping ($56M) startups peaking this year.
Tracxn Research Ad Tech Landscape, October 2016Tracxn
Investments in the adtech industry are driven by mobile, digital video, cross-screen advertising and programmatic buying targeting specific sectors or audience groups.
Tracxn Research — Enterprise Storage Landscape, November 2016Tracxn
Startups which offer backup storage solutions for enterprises emerged as the top funded business model of the year in the enterprise storage sector, accounting for 40% ($371M) of the total funding secured by the sector. The backup storage business model also witnessed a 70% YoY rise.
Tracxn Research — Insurance Tech Landscape, October 2016Tracxn
Our insurance tech report features an exhaustive Q&A with Patrick Kershaw and John Massey, Managing Partners at Leo Tech, and Andrea Traversone, Partner at Amadeus Capital Partners, who share their perspectives on market trends, investment outlook, areas of disruption, and more.
Mobile Marketing is one of 50+ sectors we track for finding highly investable startups for venture capital funds. In here, we have tracked startups across various segments like Attribution, Mobile Retargeting, Mobile Video, Native Ads, Real Time Bidding (RTB) and many more.
This sector has seen a lot of investment activity lately. The companies funded in just the last 12 months include (with total funding $): Millenial Media ($240M), Epom ($7M ), MetaMarkets ($28.5M), AppsFlyer ($7.1M ), TapStream ($700K ), TapAd (15.3M), Appier ($6M ), BlueCava ($39M ), Crosswise ($2M ), URX ($15.1M ), PlaceIQ ($27M ), Adtile ($7.2M), AdsNative ($1.5M ), Sharethrough ($28M ), Kiip ($15.4M ), MediaSpike ($5.2M ), Mnectar ($7M ), Voxel ($1.6M ), PushSpring ($1.5M),SilverPush ($1.6M ), TapCommerce ($10M ), ActionX ($5M ), Smaato ($43M), Adtheorent ($4M ), Inneractive ($14M ), Flurry ($73.3M), StrikeAd ($10.2M ), Apsalar ($14.8M ), RunAds ($1.5M ), Vungle ($25.5M )
Tracxn Research — Ecommerce Enablers Landscape, November 2016Tracxn
The total M&A activity in the year to date was valued at more than $8.5B (excluding undisclosed acquisitions), while total investments were valued at $921M.
Tracxn Research: Enterprise Security Landscape, August 2016Tracxn
Network security, endpoint security, and BYOD security are the top three funded business models in the enterprise security sector, in which 10 business models have attracted $1B+ investments.
Tracxn Research — Investment Tech Landscape, October 2016Tracxn
A number of Asian companies are among top funded in the last one year - Guangzhou-based Zhiniu8 ($150M, Series C), Singapore-based M-DAQ ($95M, Series C), Beijing-based Jusfoun Big Data ($76M, Series C), and Shanghai-based Madai Licai ($65M, PE) accounted for the top investments in the last year.
Tracxn Big Data Analytics Landscape Report, June 2016Tracxn
New Enterprise Associates, Andreessen Horowitz, Accel Partners, Intel Capital and Khosla Ventures are the top 5 investors in big data analytics, with over 10 investments each.
Tracxn Healthcare Analytics Landscape Report, September 2016Tracxn
In 2016, the healthcare analytics sector saw a significant consolidation activity with 14 major acquisitions, the largest being IBM's acquisition of Truven Health Analytics for $2.6B in February.
Harnessing the Power of Healthcare Data: Are We There YetHealth Catalyst
What can healthcare learn from Formula One racing? According to Dr. Sadiqa Mahmood, SVP of medical affairs and life sciences for Health Catalyst, race support teams leverage about 30TB of baseline data to create a digital twin of the car, track, and racer for simulation models that drive decisions at each race. Applied in the healthcare setting, a digital twin can help clinicians better understand each patient and their health conditions and circumstances in real time and make comprehensive, informed care decisions. But for the healthcare digital twin to happen, the industry must move away from data silos and towards a digital learning healthcare ecosystem.
Venture Scanner Health Tech Report Q3 2017Nathan Pacer
A report providing an overview of the Health Technology startup landscape. Includes a sector overview, graphical trends with insights, and recent funding/exit events. Contact info@venturescanner.com or visit www.venturescanner.com to learn more!
Medical Practices’ Survival Depends on Four Analytics StrategiesHealth Catalyst
With limited resources compared to large healthcare organizations and fewer personnel to shoulder burdens like COVID-19, medical practices must find ways to deliver better care with less. Delivering quality care, especially in a pandemic, is challenging, but analytics insight can guide effective care delivery methods, especially for smaller practices.
Comprehensive data combined with team members who can turn numbers into real-world information are essential for medical practices to ensure a strong financial, clinical, and operational future. Independent medical practices can rely on four analytics strategies to survive the uncertain healthcare market and plan for a sustainable future:
Prioritize access to up-to-date, comprehensive data sources.
Form a multidisciplinary approach to data governance.
Translate data into analytics insight.
Invest in analytics infrastructure to support rapid response.
HealthXL Digital Health Success Stories Report Part OneMaeve Lyons
Part 1 of HealthXL’s ‘Digital Health Success Stories’ report is now available and delves into some of the recent successes in medical tech and asks the experts what it all means.
Key Points:
Multi-million dollar investments don’t always mean success. Success looks different to each stakeholder involved in digital health.
The winners in digital health will be those who provide real solutions to problems at a reduced cost.
Part 1 of HealthXL’s ‘Digital Health Success Stories’ report comprises an in-depth view of the progress of digital health, case studies, along with opinion from some key players in the industry.
Digital health empowers us with ways to improve outcomes and increase efficiency.
Part 2 of our report will look at how we can learn from failures in digital health, available [when available and how to access].
2018 ODH Health Plan Survey - Are Health Plans Ready for Innovation?ODH, Inc.
Shifting health plan landscape brings more competition focused on performance and value. Health care payers of all kinds must innovate, manage big data and harness new technology to deliver better performance at lower costs. Early results from this Survey of 180 commercial and Medicaid health plans feature population health management challenges and solutions, the use of data aggregation as a business strategy, resources to address complex populations, and the role of technology for health plans in the shift to value-based care.
Digital Health Success Stories Report - Part 1Tom Parsons
Part 1 of HealthXL’s ‘Digital Health Success Stories’ report is now available and delves into some of the recent successes in healthcare technology and asks the experts what it all means.
Presentation on Predictive modeling in Health-care at San Jose, Ca 2015. This presentation talks about healthcare industry in US, provides stats and forecasts. It then discusses a few use cases in health care and goes into detail on a kaggle example.
April 2013 StartUp Health Insights Funding ReportStartUp Health
See StartUp Health Insights (http://www.startuphealth.com/insights) for the most comprehensive digital health funding database. Apply to StartUp Health Academy here: http://www.startuphealth.com/about-us/application/
How to Evaluate Emerging Healthcare Technology with Innovative AnalyticsHealth Catalyst
As healthcare systems are pressured to cut costs and still provide high-quality care, they will need to look across the care continuum for answers, reduce variation in care, and look to emerging technologies. This article walks through how to evaluate the safety and effectiveness and of emerging healthcare technology and prioritize high-impact improvement projects using a robust data analytics platform. Topics covered include:
The importance of identifying variation in innovation.
Ways to improve outcomes and decrease costs.
The value of an analytics platform.
The reliable information that produce sparks for innovation.
Identifying and evaluating emerging healthcare technology.
Knowing what data to use.
The difference between efficacy and effectiveness in evaluation of emerging healthcare technology.
Paddy Padmanabhan discusses the current healthcare ecosystem for the digital health startups: 'Forces driving healthcare transformation', 'Healthcare payment models', 'Focus areas and opportunities for the stakeholders', 'Investments', 'Vendor landscape' and 'Growth strategy'
A BIG DATA REVOLUTION IN HEALTH CARE SECTOR: OPPORTUNITIES, CHALLENGES AND TE...ijistjournal
Health care sector grows tremendously in last few decades. The health care sector has generated huge amounts of data that has huge volume, enormous velocity and vast variety. Also it comes from a variety of new sources as hospitals are now tend to implemented electronic health record (EHR) systems. These sources have strained the existing capabilities of existing conventional relational database management systems. In such scenario, Big data solutions offer to harness these massive, heterogeneous and complex data sets to obtain more meaningful and knowledgeable information.
This paper basically studies the impact of implementing the big data solutions on the healthcare sector, the potential opportunities, challenges and available platform and tools to implement Big data analytics in health care sector.
Using Advanced Analytics for Value-based Healthcare DeliveryMichael Joseph
Promoting Value-based Healthcare Delivery
The fundamental principles of the Affordable Care Act recognize that the volume-based, fee-for-service payment model is unsustainable and that a value-based healthcare delivery system is essential. With the emergence of Accountable Care Organizations (ACOs), providers are incentivized to implement payment reforms and participate in shared savings programs that seek to balance quality of care, access to care and cost of care.
Our healthcare analytics payment model uses predictive analytics to assist ACOs in patient attribution, budget development, bench-marking and performance monitoring to maximize incentives through shared savings and quality improvements.
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Tracxn - Southeast Asia Tech Monthly Funding ReportTracxn
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Tracxn - Top Business Models in Sustainability Tech - 26 Oct 2023Tracxn
Tracxn's proprietary #taxonomy brings to you top #BusinessModels in Top Business Models in Sustainability Tech Report https://tracxn.com/fm/dl/MnFvJ6UKhc9y
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Tracxn - United States Tech Monthly Funding ReportTracxn
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Tracxn - Top Business Models in High Tech - 27 Oct 2023Tracxn
Today's top #BusinessModel report is on Top Business Models in High Tech Report https://tracxn.com/platform/file-manager/file/ZmlsZUlkPUx5U0Qya01PRm9QbHJCTUM0UG9QVFJUeWJselF0RHJzSGU0dFlWMG5uWEk%3D
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Tracxn - Top Business Models in Vietnam Tech - 20 Oct 2023Tracxn
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Tracxn - Top Business Models in Singapore Tech - 19 Oct 2023Tracxn
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Tracxn - Top Business Models in New Zealand Tech - 19 Oct 2023Tracxn
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Tracxn - Top Business Models in United Kingdom Tech - 18 Oct 2023Tracxn
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Tracxn -Top Business Models in Transportation and Logistics Tech - 17 Oct 2023Tracxn
Tracxn's proprietary #taxonomy brings to you top #BusinessModels in Top Business Models in Transportation and Logistics Tech Report https://tracxn.com/platform/file-manager/file/ZmlsZUlkPVBPc1FQMG5DTmM2TnZwUDY4MXJmOXhFYmNZVHFJQW9jS3pURURaQ1F2MWc%3D
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Tracxn - United Kingdom Tech Monthly Funding ReportTracxn
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Tracxn - Top Business Models in Sustainability Tech - 26 Oct 2023Tracxn
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Tracxn - United States Tech Monthly Funding ReportTracxn
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Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.